Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3832
Title: Review of tool condition monitoring in machining and opportunities for deep learning
Authors: Serin, G.
Şener, B.
Özbayoğlu, Ahmet Murat
Ünver, Hakkı Özgür
Keywords: Tool condition monitoring
Machining
Industry 4
Deep multi-layer perceptron
Long-short-term memory
Convolutional neural network
Reinforcement learning
Issue Date: Jul-2020
Publisher: Springer
Source: Serin, G., Sener, B., Ozbayoglu, A. M., and Unver, H. O. (2020). Review of tool condition monitoring in machining and opportunities for deep learning. The International Journal of Advanced Manufacturing Technology, 1-22.
Abstract: Tool condition monitoring and machine tool diagnostics are performed using advanced sensors and computational intelligence to predict and avoid adverse conditions for cutting tools and machinery. Undesirable conditions during machining cause chatter, tool wear, and tool breakage, directly affecting the tool life and consequently the surface quality, dimensional accuracy of the machined parts, and tool costs. Tool condition monitoring is, therefore, extremely important for manufacturing efficiency and economics. Acoustic emission, vibration, power, and temperature sensors monitor the stability and efficiency of the machining process, collecting large amounts of data to detect tool wear, breakage, and chatter. Studies on monitoring the vibrations and acoustic emissions from machine tools have provided information and data regarding the detection of undesirable conditions. Herein, studies on tool condition monitoring are reviewed and classified. As Industry 4.0 penetrates all manufacturing sectors, the amount of manufacturing data generated has reached the level of big data, and classical artificial intelligence analyses are no longer adequate. Nevertheless, recent advances in deep learning methods have achieved revolutionary success in numerous industries. Deep multi-layer perceptron (DMLP), long-short-term memory (LSTM), convolutional neural network (CNN), and deep reinforcement learning (DRL) are among the most preferred methods of deep learning in recent years. As data size increases, these methods have shown promising performance improvement in prediction and learning, compared to classical artificial intelligence methods. This paper summarizes tool condition monitoring first, then presents the underlying theory of some of the most recent deep learning methods, and finally, attempts to identify new opportunities in tool condition monitoring, toward the realization of Industry 4.0.
URI: https://hdl.handle.net/20.500.11851/3832
https://link.springer.com/article/10.1007/s00170-020-05449-w
ISSN: 1433-3015
0268-3768
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering

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